Geodesic
Nonlinear predictive control based on neural multi-models
International Journal of Applied Mathematics and Computer Science, Tome 20 (2010) no. 1, pp. 7-21.

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
Keywords: process control, model predictive control, neural networks, optimisation, linearisation
Mots-clés : sterowanie procesami, sterowanie predykcyjne, sieć neuronowa, optymalizacja, linearyzacja 
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Ławryńczuk, M.; Tatjewski, P. Nonlinear predictive control based on neural multi-models. International Journal of Applied Mathematics and Computer Science, Tome 20 (2010) no. 1, pp. 7-21. http://geodesic.mathdoc.fr/item/IJAMCS_2010_20_1_a0/

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